Search Results for author: Elad Eban

Found 10 papers, 4 papers with code

Neighbourhood Distillation: On the benefits of non end-to-end distillation

no code implementations2 Oct 2020 Laëtitia Shao, Max Moroz, Elad Eban, Yair Movshovitz-Attias

Instead of distilling a model end-to-end, we propose to split it into smaller sub-networks - also called neighbourhoods - that are then trained independently.

Knowledge Distillation Neural Architecture Search

Fine-Grained Stochastic Architecture Search

1 code implementation17 Jun 2020 Shraman Ray Chaudhuri, Elad Eban, Hanhan Li, Max Moroz, Yair Movshovitz-Attias

Mobile neural architecture search (NAS) methods automate the design of small models but state-of-the-art NAS methods are expensive to run.

Neural Architecture Search object-detection +1

Sky Optimization: Semantically aware image processing of skies in low-light photography

1 code implementation15 Jun 2020 Orly Liba, Longqi Cai, Yun-Ta Tsai, Elad Eban, Yair Movshovitz-Attias, Yael Pritch, Huizhong Chen, Jonathan T. Barron

The sky is a major component of the appearance of a photograph, and its color and tone can strongly influence the mood of a picture.

Computationally Efficient Neural Image Compression

no code implementations18 Dec 2019 Nick Johnston, Elad Eban, Ariel Gordon, Johannes Ballé

Image compression using neural networks have reached or exceeded non-neural methods (such as JPEG, WebP, BPG).

Image Compression

Structured Multi-Hashing for Model Compression

no code implementations CVPR 2020 Elad Eban, Yair Movshovitz-Attias, Hao Wu, Mark Sandler, Andrew Poon, Yerlan Idelbayev, Miguel A. Carreira-Perpinan

Despite the success of deep neural networks (DNNs), state-of-the-art models are too large to deploy on low-resource devices or common server configurations in which multiple models are held in memory.

Model Compression

Constrained Classification and Ranking via Quantiles

no code implementations28 Feb 2018 Alan Mackey, Xiyang Luo, Elad Eban

The maximization of many of these metrics can be expressed as a constrained optimization problem, where the constraint is a function of the classifier's predictions.

Classification General Classification

Learning Max-Margin Tree Predictors

no code implementations26 Sep 2013 Ofer Meshi, Elad Eban, Gal Elidan, Amir Globerson

We demonstrate the effectiveness of our approach on several domains and show that, despite the relative simplicity of the structure, prediction accuracy is competitive with a fully connected model that is computationally costly at prediction time.

Structured Prediction

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